class_docstr: A baseline approach for computing the attribution. It multiplies input with
  the gradient with respect to input.
  https://arxiv.org/abs/1605.01713
docstr: Args:
  
      forward_func (Callable): The forward function of the model or any
                    modification of it
docstr_tok: ["Args",":","forward_func","(","Callable",")",":","The","forward","function","of","the","model","or","any","modification","of","it"]
code: def __init__(self, forward_func: Callable) -> None:
      r"""
      Args:
  
          forward_func (Callable): The forward function of the model or any
                        modification of it
      """
      GradientAttribution.__init__(self, forward_func)
  
file_path: captum/captum/attr/_core/input_x_gradient.py
imports: from typing import Any, Callable
  from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
  from captum._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import GradientAttribution
  from captum.log import log_usage
label_desc: Use image node_id 1 to create a new InputXGradient object from inherited base classes: GradientAttribution with example: obj = InputXGradient(forward_func)